28AD

Technical details

Show code
library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

Show code
kable(gpr) %>% scroll_box(width = "100%")
gdl_id keep multisensor crop_start crop_end thr_dur extent_N extent_W extent_S extent_E map_scale map_max_sample map_margin prob_map_s prob_map_s_calib prob_map_thr shift_k kernel_adjust calib_lon calib_lat calib_1_start calib_1_end calib_2_start calib_2_end calib_2_lon calib_2_lat prob_light_w thr_prob_percentile thr_gs thr_as low_speed_fix ringNo scientific_name common_name mass wing_span
28AD TRUE TRUE 2021-08-18 2022-06-30 0 0 34 -16 42 10 300 40 0.7 NA 0.9 0 1.4 39.9889 -3.37827 2021-08-18 2021-12-06 2022-04-30 2022-06-30 NA NA 0.09 0.9 120 100 15 BB6541 Halcyon senegaloides Mangrove Kingfisher NA NA

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

Show code
pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

Show code
  pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
    filter(!is.na(sta_id))

  pam$pressure %>%
    left_join(pressure_ts_bind %>% dplyr::select(c("date","pressure0")), by="date") %>%
    mutate(diff=ifelse(is.na(pressure0), 0, obs-pressure0)) %>%
    filter(sta_id > 0 & !isoutlier) %>%
    group_by(sta_id) %>%
    mutate(sta_id = paste0(sta_id, " (SD=",round(sd(diff),2)," ; N=",n(),")")) %>%
    ggplot( aes(x=diff)) +
    geom_histogram(aes(y=(..count..)/tapply(..count..,..PANEL..,sum)[..PANEL..]), binwidth=.2) +
    facet_wrap(~sta_id) +
    scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
    scale_y_continuous(name = "Normalized histogram")

Light

Show code
raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

Show code
hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

Show code
raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days")))
long_id <- which(dur > 5)

par(mfrow = c(2, 3))
for (i_s in long_id) {
  twl_fl <- twl %>%
    filter(!deleted) %>%
    filter(twilight > shortest_path_timeserie[[i_s]]$date[1] & twilight < tail(shortest_path_timeserie[[i_s]]$date, 1))
  sun <- solar(twl_fl$twilight)
  z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
  hist(z_i, freq = F, main = paste0("sta_id=", i_s, " | ", nrow(twl_fl), "twls"))
  lines(fit_z, col = "red")
  xlab("Zenith angle")
}

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

Show code
lightImage(
  tagdata = raw_geolight,
  offset = gpr$shift_k / 60 / 60
)
tsimagePoints(twl$twilight,
  offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
for (ts in shortest_path_timeserie) {
  twl_fl <- twl %>%
    filter(twilight > ts$date[1] & twilight < tail(ts$date, 1))
  if (nrow(twl_fl) > 0) {
    tsimageDeploymentLines(twl_fl$twilight,
      lon = ts$lon[1], ts$lat[1],
      offset = gpr$shift_k / 60 / 60, lwd = 3, col = adjustcolor("orange", alpha.f = 0.5)
    )
  }
}

Stationay period information

Show code
pam$sta %>% kable()
sta_id start end
1 2021-08-18 00:00:00 2021-12-05 17:00:00
2 2021-12-05 22:50:00 2021-12-07 17:50:00
3 2021-12-08 02:00:00 2021-12-09 20:00:00
4 2021-12-10 01:40:00 2021-12-10 17:40:00
5 2021-12-11 02:10:00 2021-12-11 21:20:00
6 2021-12-11 22:20:00 2021-12-16 19:40:00
7 2021-12-17 01:40:00 2022-01-04 19:40:00
8 2022-01-04 23:20:00 2022-04-27 18:20:00
9 2022-04-28 02:00:00 2022-04-28 16:50:00
10 2022-04-29 01:40:00 2022-04-29 20:50:00
11 2022-04-29 23:00:00 2022-06-29 23:50:00